Deep Learning based Car Identification

- Automotive, Surveillance, Object Detection & Localisation

Project By:
Premjeet Kumar https://www.linkedin.com/in/premjeet-kumar/
Hari Samynaath S https://www.linkedin.com/in/harinaathan/
Veena Raju https://www.linkedin.com/in/veena-raju-1b16b513b/
Javed Bhai https://www.linkedin.com/in/javedbhai/
Surabhi Joshi https://www.linkedin.com/in/surabhi-joshi-4452788/

Project For:
Captstone project for Post Graduate Program in Artificial Intelligence and Machine Learning
with GreatLakes & Texas McCombs School of Business, The University of Texas at Austin

CONTEXT:
Computer vision can be used to automate supervision and generate action appropriate action trigger if the event is predicted from the image of interest. For example a car moving on the road can be easily identi ied by a camera as make of the car, type, colour, number plates etc.

DATA DESCRIPTION:
The Cars dataset contains 16,185 images of 196 classes of cars. The data is split into 8,144 training images and 8,041 testing images, where each class has been split roughly in a 50-50 split. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe.

‣ Train Images: Consists of real images of cars as per the make and year of the car.
‣ Test Images: Consists of real images of cars as per the make and year of the car.
‣ Train Annotation: Consists of bounding box region for training images.
‣ Test Annotation: Consists of bounding box region for testing images.

MILESTONE 1:

‣ Step 1: Import the data
‣ Step 2: Map training and testing images to its classes.
‣ Step 3: Map training and testing images to its annotations.
‣ Step 4: Display images with bounding box
‣ Output: Images mapped to its class and annotation ready to be used for deep learning

MILESTONE 2:

‣ Input: Output of milestone 1
‣ Step 1: Design, train and test CNN models to classify the car.
‣ Step 2: Design, train and test RCNN & its hybrids based object detection models to impose the bounding box or mask over the area of interest.
‣ Output: Pickled model to be used for future prediction

import tensorflow as tf
import numpy as np
import os, pickle, re
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
pd.set_option('display.max_columns', 50)

# import models to environment
from tensorflow.keras.optimizers import Adagrad,Adam,SGD,RMSprop
from tensorflow.keras import regularizers
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg19 import VGG19
from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.applications.resnet_v2 import ResNet152V2,ResNet50V2,ResNet101V2
from tensorflow.keras.applications.efficientnet_v2 import EfficientNetV2B0,EfficientNetV2B1,EfficientNetV2B2,EfficientNetV2B3
from tensorflow.keras.applications.efficientnet_v2 import EfficientNetV2S,EfficientNetV2M,EfficientNetV2L
from tensorflow.keras.applications.densenet import DenseNet169,DenseNet201,DenseNet121
from tensorflow.keras.applications.nasnet import NASNetMobile

# list down parameters and the test ranges
optims = [Adam(), SGD(momentum=0.1)]

patiences = [4,8]
slopes = [0.3,0.5]
factors = [0.1,0.3]

uFreezePercents = [0,0.3,0.7]

learningRates = [1e-3,1e-7]
lossBalancing = [0.2,0.8]

dOuts = [0.5,0.7]
lmbdas = [0.1,0.01]

# create grid of parameters
pGrid = [{'optimizer':o,
          'patience':p,'slope':s,'factor':f,
          'unFreezeRatio':r,
          'learning_rate':lr,'lossBalance':lb,
          'dropOut':do,'lmbda':lmb}
         for o in optims
         for p in patiences for s in slopes for f in factors
         for r in uFreezePercents
         for lr in learningRates for lb in lossBalancing
         for do in dOuts for lmb in lmbdas
        ]
print("Parameter grid count: {:,d}".format(len(pGrid)))

Parameter grid count: 768

# define a transfer learning network mapped to our targets
def transferNET(application,input_size,dOut,lmbda):    
    
    import tensorflow as tf
    from tensorflow.keras.models import Model
    from tensorflow.keras.layers import Input,GlobalMaxPool2D,Dense,BatchNormalization,Dropout
    
     # Random consistency seed
    tf.random.set_seed(100)
    
    # load application
    tNet = application(input_shape=input_size[1:],include_top=False, weights='imagenet')
    
    # flatten with pooling
    pool = GlobalMaxPool2D(name="CustomLayerStart")(tNet.output)
    
    # classifier branch for car names
    nameBranch = Dense(512,activation='relu',kernel_regularizer=regularizers.l2(lmbda))(pool)
    Nbn1 = BatchNormalization()(nameBranch)
    Ndo1 = Dropout(dOut)(Nbn1)
    Nhid1 = Dense(256,activation='relu',kernel_regularizer=regularizers.l2(lmbda))(Ndo1)
    Nbn2 = BatchNormalization()(Nhid1)
    Ndo2 = Dropout(dOut)(Nbn2)
    classifier = Dense(196,activation='softmax',name="names")(Ndo2)
    
    # regression branch for bounding boxes
    boxBranch = Dense(64,activation='relu',kernel_regularizer=regularizers.l2(lmbda))(pool)
    Bbn1 = BatchNormalization()(boxBranch)
    Bdo1 = Dropout(dOut)(Bbn1)
    Bhid1 = Dense(32,activation='relu',kernel_regularizer=regularizers.l2(lmbda))(Bdo1)
    Bbn2 = BatchNormalization()(Bhid1)
    Bdo2 = Dropout(dOut)(Bbn2)
    bBox = Dense(4,activation='relu',name="boxes")(Bdo2)
    
    # assemble the network
    model = Model(inputs=tNet.inputs,outputs=[classifier,bBox])
    
    # freeze application layers and open classifer & regressor for training
    for layer in model.layers[:-15]:
        layer.trainable = False
    
    return model

models = [VGG16,VGG19,MobileNetV2,ResNet50V2,EfficientNetV2S,DenseNet201,NASNetMobile]
compareResults=pd.DataFrame()
for model in models:
    fname = os.path.join('deployables',model.__name__,"%s_attrib.gl"%model.__name__)
    with open(fname, 'rb') as fh:
        attributes = pickle.load(fh)
    """if compareResults.shape[0]==0:
        compareResults = pd.DataFrame(columns=list(attributes['evalResults'].keys()),dtype=object)"""
    
    compareResults = compareResults.append(attributes['evalResults'],ignore_index=True)
    
compareResults

	name	target_size	batch_size	optimizer	patience	slope	factor	learning_rate	lossWeights	epochs	verbose	logs	loss	names_loss	boxes_loss	names_CategoricalAccuracy	names_Precision	names_Recall	boxes_IoU	val_loss	val_names_loss	val_boxes_loss	val_names_CategoricalAccuracy	val_names_Precision	val_names_Recall	val_boxes_IoU	steps_per_epoch	validation_steps
0	VGG16	(224, 224)	32	Adam	15	0.15	0.1	0.001	{'names': 0.5, 'boxes': 0.5}	30	1	{'loss': [5.587075233459473, 3.966358661651611...	0.844168	0.831028	0.013140	0.859375	0.978552	0.656742	0.646943	2.857366	2.844716	0.012650	0.327067	0.650644	0.163471	0.651080	NaN	NaN
1	VGG19	(224, 224)	32	Adam	15	0.15	0.1	0.001	{'names': 0.5, 'boxes': 0.5}	30	1	{'loss': [5.638175010681152, 4.074068546295166...	0.952510	0.937004	0.015506	0.838091	0.980834	0.604454	0.618246	3.030715	3.015413	0.015301	0.303909	0.626506	0.135956	0.620036	NaN	NaN
2	MobileNetV2	(224, 224)	32	Adam	15	0.15	0.1	0.001	{'names': 0.5, 'boxes': 0.5}	30	1	{'loss': [5.118948459625244, 3.445307493209839...	0.452112	0.440491	0.011621	0.928765	0.982900	0.869833	0.650052	2.506006	2.494308	0.011697	0.389442	0.670391	0.224104	0.647654	NaN	NaN
3	ResNet50V2	(224, 224)	32	Adam	15	0.15	0.1	0.001	{'names': 0.5, 'boxes': 0.5}	30	1	{'loss': [5.000739574432373, 3.436673164367676...	0.369423	0.350934	0.018489	0.935778	0.981474	0.912525	0.587236	2.480897	2.462386	0.018511	0.408989	0.657504	0.259089	0.589307	NaN	NaN
4	EfficientNetV2S	(224, 224)	32	Adam	15	0.15	0.1	0.001	{'names': 0.5, 'boxes': 0.5}	30	1	{'loss': [5.972702503204346, 5.568402290344238...	4.983481	4.955740	0.027741	0.037032	0.000000	0.000000	0.511724	5.245408	5.218029	0.027380	0.016434	0.000000	0.000000	0.514779	NaN	NaN
5	DenseNet201	(224, 224)	32	Adam	15	0.15	0.1	0.001	{'names': 0.5, 'boxes': 0.5}	2	1	{'loss': [5.747513771057129, 4.238009452819824...	3.653059	3.343921	0.309137	0.230315	0.659529	0.037894	0.004085	4.151174	3.840998	0.310175	0.139940	0.412234	0.019298	0.029028	100.0	10.0
6	NASNetMobile	(224, 224)	32	Adam	15	0.15	0.1	0.001	{'names': 0.5, 'boxes': 0.5}	30	1	{'loss': [4.982983589172363, 3.866465091705322...	1.269307	1.258898	0.010408	0.728716	0.958167	0.414247	0.667567	2.815521	2.804810	0.010710	0.305154	0.639756	0.117405	0.665775	NaN	NaN

KPIs = compareResults[['name','loss','names_CategoricalAccuracy','boxes_IoU',
                                'val_loss','val_names_CategoricalAccuracy','val_boxes_IoU']]
KPIs.sort_values(by='val_names_CategoricalAccuracy',ascending=False)

	name	loss	names_CategoricalAccuracy	boxes_IoU	val_loss	val_names_CategoricalAccuracy	val_boxes_IoU
3	ResNet50V2	0.369423	0.935778	0.587236	2.480897	0.408989	0.589307
2	MobileNetV2	0.452112	0.928765	0.650052	2.506006	0.389442	0.647654
0	VGG16	0.844168	0.859375	0.646943	2.857366	0.327067	0.651080
6	NASNetMobile	1.269307	0.728716	0.667567	2.815521	0.305154	0.665775
1	VGG19	0.952510	0.838091	0.618246	3.030715	0.303909	0.620036
5	DenseNet201	3.653059	0.230315	0.004085	4.151174	0.139940	0.029028
4	EfficientNetV2S	4.983481	0.037032	0.511724	5.245408	0.016434	0.514779

from lib.networkPipe import Pipe

2022-07-31 14:00:48.023382: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-07-31 14:00:48.049584: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-07-31 14:00:48.049842: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-07-31 14:00:48.050442: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-07-31 14:00:48.050906: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-07-31 14:00:48.051185: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-07-31 14:00:48.051315: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-07-31 14:00:48.415168: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-07-31 14:00:48.415373: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-07-31 14:00:48.415484: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-07-31 14:00:48.415576: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3203 MB memory:  -> device: 0, name: Quadro P1000, pci bus id: 0000:01:00.0, compute capability: 6.1

logdir = "logs"
%load_ext tensorboard
%tensorboard --logdir logs

gIndex = np.arange(len(pGrid))
for index in np.random.choice(gIndex,30,replace=False):
    
    print("gridPosition used : %03d"%index)
    
    gPoint = pGrid[index]    
    # generator parameters
    gParams = dict(target_size=(224,224),batch_size=32)
    # model creation parameters
    mParams = dict(application=ResNet50V2,dOut=gPoint.get('dropOut'),lmbda=gPoint.get('lmbda'))
    # optimiser for compilation
    optim = gPoint.get('optimizer')
    # lr scheduler parameters
    lrParams = dict(patience=gPoint.get('patience'),slope=gPoint.get('slope'),factor=gPoint.get('factor'))
    # earlyStop parameters
    #eSparams = dict(patience=int(gPoint.get('patience')*3.1),slope=gPoint.get('slope')*0.5)
    eSparams = dict()
    # model fitting parameters
    fParams = dict(epochs=30,verbose=0,steps_per_epoch=50,validation_steps=10)
    # trainability scheduler parameters
    if gPoint.get('unFreezeRatio')==0:
        trParams = dict() 
    else:
        trParams = dict(uncontrolled=-15,schedule={int(gPoint.get('patience')*2.8):gPoint.get('unFreezeRatio')})
    # other hyper parameters
    lRate = gPoint.get('learning_rate')
    lBalance = gPoint.get('lossBalance')
    gridPoint = dict(learning_rate=lRate,lossWeights={'names':lBalance,'boxes':1-lBalance})
    
    pp = Pipe(generatorParams=gParams,modelFunc=transferNET,modelName="ResNet50V2_hTune_%03d"%index,modelParams=mParams)
    pp.compiler(optim=optim,lrScheduleParams=lrParams,eStopParams=eSparams,
                trainabilityParams=trParams,gridPoint=gridPoint,sBest=False)
    pp.fit(gridPoint=gridPoint,fitParams=fParams)
    pp.evaluate()
    pp.htuneSave()

gridPosition used : 489

imScanTrain: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 196/196 [00:04<00:00, 47.64it/s]
imScanTest: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 196/196 [00:03<00:00, 53.40it/s]
2022-07-30 00:32:38.326589: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
2022-07-30 00:32:40.018558: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 00:33:10.028114: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 487

2022-07-30 00:55:47.512971: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 00:56:16.839976: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 146

2022-07-30 01:19:09.548922: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 01:19:40.540491: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 672

2022-07-30 01:42:54.852345: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 01:43:24.809501: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 323

2022-07-30 02:06:30.394239: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 02:07:01.609770: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 758

2022-07-30 02:30:26.503942: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 02:30:56.733865: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 123

2022-07-30 02:53:57.342220: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 02:54:28.164741: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 643

2022-07-30 03:17:31.700605: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 03:18:02.071899: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 588

2022-07-30 03:41:00.600867: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 03:41:31.449377: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 097

2022-07-30 04:04:47.923868: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 04:05:17.612968: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 622

2022-07-30 04:28:41.150176: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 04:29:10.795849: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 132

2022-07-30 04:52:45.528712: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 04:53:16.602101: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 661

2022-07-30 05:16:41.133705: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 05:17:12.871830: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 689

2022-07-30 05:40:35.326024: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 05:41:06.266506: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 279

2022-07-30 06:04:17.981954: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 06:04:49.245426: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 750

2022-07-30 06:27:57.965199: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 06:28:28.111382: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 545

2022-07-30 06:51:36.246675: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 06:52:06.573275: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 516

2022-07-30 07:15:26.421792: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 07:15:56.859499: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 164

2022-07-30 07:39:13.704803: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 07:39:45.410792: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 043

2022-07-30 08:03:14.665465: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 08:03:47.117854: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 280

2022-07-30 08:27:32.191586: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 08:28:02.864992: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 281

2022-07-30 08:51:45.417142: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 08:52:16.936634: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 717

2022-07-30 09:16:05.382913: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 09:16:37.109896: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 526

2022-07-30 09:40:09.228798: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 09:40:41.263332: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 755

2022-07-30 10:04:06.632067: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 10:04:37.544375: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 562

2022-07-30 10:28:08.456790: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 10:28:39.289292: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 539

2022-07-30 10:51:21.774174: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 10:51:52.093384: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 042

2022-07-30 11:14:39.770655: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 11:15:11.939027: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 582

2022-07-30 11:38:29.403042: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 11:39:00.873826: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

gridPosition used : 002

2022-07-30 12:02:11.071999: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
2022-07-30 12:02:42.897864: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

dirList = os.listdir('./deployables/')
tuneList = [os.path.join('./deployables',fname) 
            for fname in dirList 
            if re.search(r'(hTune)',fname) 
            and re.search(r'(attrib.gl)',fname)]

tuneLog = pd.DataFrame()
for fname in tuneList:
    with open(fname, 'rb') as fh:
        attributes = pickle.load(fh)        
    tuneLog = tuneLog.append(attributes['evalResults'],ignore_index=True)

KPIs = tuneLog[['name','loss','names_CategoricalAccuracy','boxes_IoU',
                'val_loss','val_names_CategoricalAccuracy','val_boxes_IoU']]
KPIs.sort_values(by='val_names_CategoricalAccuracy',ascending=False)

	name	loss	names_CategoricalAccuracy	boxes_IoU	val_loss	val_names_CategoricalAccuracy	val_boxes_IoU
7	ResNet50V2_hTune_357	3.096646	0.516609	0.647384	3.713136	0.309512	0.648093
23	ResNet50V2_hTune_213	4.162242	0.515010	0.395893	4.792305	0.303909	0.399777
22	ResNet50V2_hTune_164	3.724387	0.362082	0.606193	4.090173	0.223481	0.609967
50	ResNet50V2_hTune_097	4.609859	0.317667	0.508563	4.932710	0.217505	0.512276
30	ResNet50V2_hTune_279	4.098151	0.301058	0.481427	4.343278	0.213770	0.482655
20	ResNet50V2_hTune_132	4.763387	0.244094	0.572507	4.990209	0.183391	0.576064
5	ResNet50V2_hTune_323	4.761096	0.245940	0.400854	4.944709	0.182271	0.404170
4	ResNet50V2_hTune_291	4.787927	0.240773	0.456392	4.985533	0.180777	0.465225
21	ResNet50V2_hTune_146	6.324769	0.252584	0.013193	6.519183	0.174801	0.012665
18	ResNet50V2_hTune_102	5.848691	0.136319	0.281144	5.945723	0.115289	0.289173
0	ResNet50V2_hTune_002	5.572777	0.136811	0.296225	5.664396	0.109437	0.297342
39	ResNet50V2_hTune_661	18.329708	0.029158	0.015101	18.433784	0.019671	-0.041360
43	ResNet50V2_hTune_705	18.430031	0.024360	-0.005257	18.544445	0.017804	-0.163723
40	ResNet50V2_hTune_672	109.464905	0.023499	0.026671	109.588593	0.016434	-0.092030
44	ResNet50V2_hTune_709	18.413792	0.023130	-0.003484	18.497936	0.015189	-0.020716
42	ResNet50V2_hTune_689	18.469704	0.020300	-0.086530	18.557299	0.014318	-0.000630
38	ResNet50V2_hTune_643	18.459730	0.017963	-0.026938	18.516497	0.014193	0.019272
48	ResNet50V2_hTune_758	103.801414	0.016855	-0.064730	103.871284	0.013944	0.036628
33	ResNet50V2_hTune_582	109.715393	0.014764	-0.035304	109.772369	0.013073	0.015242
47	ResNet50V2_hTune_755	18.503374	0.016486	-0.047379	18.565781	0.012824	-0.114791
17	ResNet50V2_hTune_545	18.663509	0.016609	-0.005747	18.740150	0.012201	-0.026340
51	ResNet50V2_hTune_562	116.498932	0.012672	-0.168092	116.531418	0.011205	0.034437
34	ResNet50V2_hTune_583	18.495226	0.012918	0.043513	18.552563	0.010334	0.033378
13	ResNet50V2_hTune_514	120.375450	0.008858	0.002731	120.406662	0.009711	-0.008530
10	ResNet50V2_hTune_487	18.823624	0.009843	-0.026517	18.851259	0.008964	0.002790
14	ResNet50V2_hTune_516	120.422836	0.013287	-0.028129	120.495956	0.008964	-0.116535
8	ResNet50V2_hTune_439	18.653530	0.010089	-0.039540	18.687246	0.007844	0.071707
12	ResNet50V2_hTune_511	19.170870	0.005167	0.029037	19.167377	0.006599	0.085640
29	ResNet50V2_hTune_270	137.937729	0.004798	0.058051	137.935181	0.006599	0.120691
26	ResNet50V2_hTune_223	19.146347	0.005413	-0.025036	19.145479	0.006474	-0.114905
24	ResNet50V2_hTune_219	19.155247	0.005044	3.815772	19.152126	0.006474	0.041418
35	ResNet50V2_hTune_587	19.170319	0.005167	0.044977	19.166660	0.006474	0.917248
1	ResNet50V2_hTune_010	138.075211	0.004921	-3.453720	138.071732	0.006350	-0.404825
49	ResNet50V2_hTune_762	138.384766	0.004675	0.088330	138.382492	0.006101	0.376361
6	ResNet50V2_hTune_330	177.489014	0.004675	6.251137	177.330978	0.005976	4.197583
27	ResNet50V2_hTune_232	138.105576	0.005536	-2.019621	138.103317	0.005852	0.651765
41	ResNet50V2_hTune_681	19.364851	0.005536	0.215715	19.361935	0.005852	0.171500
3	ResNet50V2_hTune_043	19.179884	0.005906	0.070257	19.181965	0.005603	-0.066094
28	ResNet50V2_hTune_233	19.342075	0.005659	-0.291289	19.343296	0.005603	0.020349
25	ResNet50V2_hTune_220	138.112259	0.005044	-0.335361	138.109329	0.005603	-0.057849
46	ResNet50V2_hTune_750	138.605545	0.005044	0.106731	138.608398	0.005478	0.289205
9	ResNet50V2_hTune_445	19.363623	0.005659	0.168821	19.359472	0.005478	-6.152737
37	ResNet50V2_hTune_622	138.613068	0.005290	0.118651	138.614532	0.005478	-0.029541
32	ResNet50V2_hTune_281	19.370234	0.004921	0.115642	19.381521	0.005478	-0.269149
16	ResNet50V2_hTune_539	19.186636	0.004921	2.096231	19.190557	0.005354	0.050004
15	ResNet50V2_hTune_526	138.612183	0.005044	0.080575	138.614792	0.005354	-0.337572
2	ResNet50V2_hTune_042	138.296600	0.005290	-3.407888	138.299850	0.005354	-0.102929
11	ResNet50V2_hTune_489	19.385286	0.004921	0.286832	19.393044	0.005105	0.046976
36	ResNet50V2_hTune_588	138.818680	0.005044	0.135574	138.826462	0.005105	0.019738
31	ResNet50V2_hTune_280	138.274414	0.004552	0.345136	138.284317	0.005105	0.488681
45	ResNet50V2_hTune_717	19.394646	0.004552	0.507070	19.399637	0.004980	-0.084612
19	ResNet50V2_hTune_123	19.180258	0.005413	0.266682	19.185755	0.004980	0.032024

pGrid[357]

{'optimizer': <keras.optimizers.optimizer_v2.adam.Adam at 0x7f69582c7ee0>,
 'patience': 8,
 'slope': 0.5,
 'factor': 0.3,
 'unFreezeRatio': 0.3,
 'learning_rate': 0.001,
 'lossBalance': 0.8,
 'dropOut': 0.5,
 'lmbda': 0.01}

index=357

gPoint = pGrid[index]    
# generator parameters
gParams = dict(target_size=(224,224),batch_size=32)
# model creation parameters
mParams = dict(application=ResNet50V2,dOut=gPoint.get('dropOut'),lmbda=gPoint.get('lmbda'))
# optimiser for compilation
optim = gPoint.get('optimizer')
# lr scheduler parameters
lrParams = dict(patience=gPoint.get('patience'),slope=gPoint.get('slope'),factor=gPoint.get('factor'))
# earlyStop parameters
eSparams = dict(patience=int(gPoint.get('patience')*3.1),slope=gPoint.get('slope')*0.5)
# model fitting parameters
fParams = dict(epochs=40,verbose=1)
# trainability scheduler parameters
if gPoint.get('unFreezeRatio')==0:
    trParams = dict() 
else:
    trParams = dict(uncontrolled=-15,schedule={int(gPoint.get('patience')*2.8):gPoint.get('unFreezeRatio')})
# other hyper parameters
lRate = gPoint.get('learning_rate')
lBalance = gPoint.get('lossBalance')
gridPoint = dict(learning_rate=lRate,lossWeights={'names':lBalance,'boxes':1-lBalance})

pp = Pipe(generatorParams=gParams,modelFunc=transferNET,modelName="ResNet50V2_FINAL",modelParams=mParams)
pp.compiler(optim=optim,lrScheduleParams=lrParams,eStopParams=eSparams,
            trainabilityParams=trParams,gridPoint=gridPoint,sBest=False)
pp.fit(gridPoint=gridPoint,fitParams=fParams)
pp.evaluate()
pp.save()
pp.report()

imScanTrain: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 196/196 [00:03<00:00, 51.07it/s]
imScanTest: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 196/196 [00:03<00:00, 52.80it/s]

Epoch 1/40

2022-07-30 16:00:58.213178: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
2022-07-30 16:00:59.958693: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

229/229 [==============================] - ETA: 0s - loss: 13.3685 - names_loss: 5.1055 - boxes_loss: 0.6026 - names_CategoricalAccuracy: 0.0424 - names_Precision: 0.2404 - names_Recall: 0.0034 - boxes_IoU: -0.0935

2022-07-30 16:03:12.492087: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

229/229 [==============================] - 151s 638ms/step - loss: 13.3685 - names_loss: 5.1055 - boxes_loss: 0.6026 - names_CategoricalAccuracy: 0.0424 - names_Precision: 0.2404 - names_Recall: 0.0034 - boxes_IoU: -0.0935 - val_loss: 9.4936 - val_names_loss: 4.2723 - val_boxes_loss: 0.2507 - val_names_CategoricalAccuracy: 0.0925 - val_names_Precision: 0.6667 - val_names_Recall: 0.0075 - val_boxes_IoU: 0.0758 - learning_rate: 0.0010 - Trainable: 15.0000 - lapTime: 151.0182
Epoch 2/40
229/229 [==============================] - 148s 648ms/step - loss: 8.0431 - names_loss: 4.0030 - boxes_loss: 0.2577 - names_CategoricalAccuracy: 0.1161 - names_Precision: 0.3875 - names_Recall: 0.0127 - boxes_IoU: 0.0533 - val_loss: 6.9119 - val_names_loss: 3.8428 - val_boxes_loss: 0.1362 - val_names_CategoricalAccuracy: 0.1212 - val_names_Precision: 0.4762 - val_names_Recall: 0.0125 - val_boxes_IoU: 0.2473 - learning_rate: 0.0010 - Trainable: 15.0000 - lapTime: 148.4243
Epoch 3/40
229/229 [==============================] - 145s 634ms/step - loss: 6.2374 - names_loss: 3.6105 - boxes_loss: 0.1303 - names_CategoricalAccuracy: 0.1552 - names_Precision: 0.4567 - names_Recall: 0.0237 - boxes_IoU: 0.3130 - val_loss: 5.9422 - val_names_loss: 3.7712 - val_boxes_loss: 0.0357 - val_names_CategoricalAccuracy: 0.1312 - val_names_Precision: 0.3824 - val_names_Recall: 0.0162 - val_boxes_IoU: 0.4923 - learning_rate: 0.0010 - Trainable: 15.0000 - lapTime: 145.0111
Epoch 4/40
229/229 [==============================] - 145s 633ms/step - loss: 5.3820 - names_loss: 3.3799 - boxes_loss: 0.0613 - names_CategoricalAccuracy: 0.1953 - names_Precision: 0.5085 - names_Recall: 0.0328 - boxes_IoU: 0.4024 - val_loss: 5.4368 - val_names_loss: 3.6268 - val_boxes_loss: 0.0307 - val_names_CategoricalAccuracy: 0.1650 - val_names_Precision: 0.5962 - val_names_Recall: 0.0388 - val_boxes_IoU: 0.4930 - learning_rate: 0.0010 - Trainable: 15.0000 - lapTime: 144.9356
Epoch 5/40
229/229 [==============================] - 146s 636ms/step - loss: 4.8714 - names_loss: 3.1954 - boxes_loss: 0.0357 - names_CategoricalAccuracy: 0.2192 - names_Precision: 0.5275 - names_Recall: 0.0445 - boxes_IoU: 0.4930 - val_loss: 4.9950 - val_names_loss: 3.4277 - val_boxes_loss: 0.0255 - val_names_CategoricalAccuracy: 0.2050 - val_names_Precision: 0.6038 - val_names_Recall: 0.0400 - val_boxes_IoU: 0.5268 - learning_rate: 0.0010 - Trainable: 15.0000 - lapTime: 145.5701
Epoch 6/40
229/229 [==============================] - 145s 636ms/step - loss: 4.5483 - names_loss: 3.0709 - boxes_loss: 0.0260 - names_CategoricalAccuracy: 0.2380 - names_Precision: 0.5627 - names_Recall: 0.0588 - boxes_IoU: 0.5402 - val_loss: 4.9250 - val_names_loss: 3.4961 - val_boxes_loss: 0.0244 - val_names_CategoricalAccuracy: 0.1863 - val_names_Precision: 0.4746 - val_names_Recall: 0.0350 - val_boxes_IoU: 0.5511 - learning_rate: 0.0010 - Trainable: 15.0000 - lapTime: 145.4441
Epoch 7/40
229/229 [==============================] - 144s 631ms/step - loss: 4.3590 - names_loss: 2.9631 - boxes_loss: 0.0221 - names_CategoricalAccuracy: 0.2501 - names_Precision: 0.5931 - names_Recall: 0.0678 - boxes_IoU: 0.5642 - val_loss: 4.6417 - val_names_loss: 3.2594 - val_boxes_loss: 0.0262 - val_names_CategoricalAccuracy: 0.2250 - val_names_Precision: 0.5797 - val_names_Recall: 0.0500 - val_boxes_IoU: 0.5251 - learning_rate: 0.0010 - Trainable: 15.0000 - lapTime: 144.4364
Epoch 8/40
229/229 [==============================] - 145s 634ms/step - loss: 4.2745 - names_loss: 2.9165 - boxes_loss: 0.0201 - names_CategoricalAccuracy: 0.2672 - names_Precision: 0.5812 - names_Recall: 0.0757 - boxes_IoU: 0.5769 - val_loss: 4.7216 - val_names_loss: 3.3661 - val_boxes_loss: 0.0182 - val_names_CategoricalAccuracy: 0.1837 - val_names_Precision: 0.4375 - val_names_Recall: 0.0437 - val_boxes_IoU: 0.5798 - learning_rate: 0.0010 - Trainable: 15.0000 - lapTime: 145.1864
Epoch 9/40
229/229 [==============================] - 145s 632ms/step - loss: 4.1094 - names_loss: 2.7838 - boxes_loss: 0.0176 - names_CategoricalAccuracy: 0.2851 - names_Precision: 0.6039 - names_Recall: 0.0940 - boxes_IoU: 0.5942 - val_loss: 4.6738 - val_names_loss: 3.3673 - val_boxes_loss: 0.0182 - val_names_CategoricalAccuracy: 0.1725 - val_names_Precision: 0.5833 - val_names_Recall: 0.0262 - val_boxes_IoU: 0.5980 - learning_rate: 0.0010 - Trainable: 15.0000 - lapTime: 144.7306
Epoch 10/40
229/229 [==============================] - 145s 634ms/step - loss: 3.9685 - names_loss: 2.7039 - boxes_loss: 0.0166 - names_CategoricalAccuracy: 0.2982 - names_Precision: 0.6122 - names_Recall: 0.1013 - boxes_IoU: 0.6031 - val_loss: 4.5684 - val_names_loss: 3.3219 - val_boxes_loss: 0.0159 - val_names_CategoricalAccuracy: 0.1962 - val_names_Precision: 0.4368 - val_names_Recall: 0.0475 - val_boxes_IoU: 0.5989 - learning_rate: 0.0010 - Trainable: 15.0000 - lapTime: 145.1468
Epoch 11/40
229/229 [==============================] - 145s 635ms/step - loss: 3.3925 - names_loss: 2.2519 - boxes_loss: 0.0137 - names_CategoricalAccuracy: 0.4032 - names_Precision: 0.7711 - names_Recall: 0.1531 - boxes_IoU: 0.6301 - val_loss: 3.8086 - val_names_loss: 2.7547 - val_boxes_loss: 0.0166 - val_names_CategoricalAccuracy: 0.3212 - val_names_Precision: 0.6883 - val_names_Recall: 0.0662 - val_boxes_IoU: 0.5988 - learning_rate: 3.0000e-04 - Trainable: 15.0000 - lapTime: 145.4557
Epoch 12/40
229/229 [==============================] - 150s 655ms/step - loss: 3.0395 - names_loss: 2.0521 - boxes_loss: 0.0127 - names_CategoricalAccuracy: 0.4479 - names_Precision: 0.7793 - names_Recall: 0.1870 - boxes_IoU: 0.6432 - val_loss: 3.8984 - val_names_loss: 2.9639 - val_boxes_loss: 0.0130 - val_names_CategoricalAccuracy: 0.2525 - val_names_Precision: 0.5614 - val_names_Recall: 0.0400 - val_boxes_IoU: 0.6411 - learning_rate: 3.0000e-04 - Trainable: 15.0000 - lapTime: 149.9832
Epoch 13/40
229/229 [==============================] - 144s 630ms/step - loss: 2.8282 - names_loss: 1.9312 - boxes_loss: 0.0121 - names_CategoricalAccuracy: 0.4784 - names_Precision: 0.7882 - names_Recall: 0.2103 - boxes_IoU: 0.6496 - val_loss: 3.5944 - val_names_loss: 2.7280 - val_boxes_loss: 0.0122 - val_names_CategoricalAccuracy: 0.3113 - val_names_Precision: 0.6211 - val_names_Recall: 0.0737 - val_boxes_IoU: 0.6422 - learning_rate: 3.0000e-04 - Trainable: 15.0000 - lapTime: 144.2246
Epoch 14/40
229/229 [==============================] - 146s 638ms/step - loss: 2.6739 - names_loss: 1.8332 - boxes_loss: 0.0118 - names_CategoricalAccuracy: 0.5026 - names_Precision: 0.8043 - names_Recall: 0.2434 - boxes_IoU: 0.6545 - val_loss: 3.5363 - val_names_loss: 2.7157 - val_boxes_loss: 0.0125 - val_names_CategoricalAccuracy: 0.3225 - val_names_Precision: 0.6630 - val_names_Recall: 0.0763 - val_boxes_IoU: 0.6400 - learning_rate: 3.0000e-04 - Trainable: 15.0000 - lapTime: 146.0795
Epoch 15/40
229/229 [==============================] - 144s 630ms/step - loss: 2.5532 - names_loss: 1.7529 - boxes_loss: 0.0111 - names_CategoricalAccuracy: 0.5265 - names_Precision: 0.8121 - names_Recall: 0.2672 - boxes_IoU: 0.6599 - val_loss: 3.3768 - val_names_loss: 2.5915 - val_boxes_loss: 0.0109 - val_names_CategoricalAccuracy: 0.3262 - val_names_Precision: 0.6545 - val_names_Recall: 0.0900 - val_boxes_IoU: 0.6684 - learning_rate: 3.0000e-04 - Trainable: 15.0000 - lapTime: 144.4761
Epoch 16/40
229/229 [==============================] - 144s 631ms/step - loss: 2.4636 - names_loss: 1.6879 - boxes_loss: 0.0110 - names_CategoricalAccuracy: 0.5418 - names_Precision: 0.8109 - names_Recall: 0.2791 - boxes_IoU: 0.6612 - val_loss: 3.5229 - val_names_loss: 2.7588 - val_boxes_loss: 0.0097 - val_names_CategoricalAccuracy: 0.3200 - val_names_Precision: 0.5556 - val_names_Recall: 0.0750 - val_boxes_IoU: 0.6722 - learning_rate: 3.0000e-04 - Trainable: 15.0000 - lapTime: 144.2964
Epoch 17/40
229/229 [==============================] - 146s 636ms/step - loss: 2.3512 - names_loss: 1.5961 - boxes_loss: 0.0106 - names_CategoricalAccuracy: 0.5665 - names_Precision: 0.8377 - names_Recall: 0.3114 - boxes_IoU: 0.6667 - val_loss: 3.4201 - val_names_loss: 2.6732 - val_boxes_loss: 0.0107 - val_names_CategoricalAccuracy: 0.3125 - val_names_Precision: 0.6288 - val_names_Recall: 0.1037 - val_boxes_IoU: 0.6613 - learning_rate: 3.0000e-04 - Trainable: 15.0000 - lapTime: 145.4811
Epoch 18/40
229/229 [==============================] - 146s 638ms/step - loss: 2.2986 - names_loss: 1.5603 - boxes_loss: 0.0103 - names_CategoricalAccuracy: 0.5636 - names_Precision: 0.8225 - names_Recall: 0.3206 - boxes_IoU: 0.6669 - val_loss: 3.5708 - val_names_loss: 2.8150 - val_boxes_loss: 0.0333 - val_names_CategoricalAccuracy: 0.2862 - val_names_Precision: 0.5660 - val_names_Recall: 0.1125 - val_boxes_IoU: 0.4339 - learning_rate: 3.0000e-04 - Trainable: 15.0000 - lapTime: 146.1071
Epoch 19/40
229/229 [==============================] - 146s 637ms/step - loss: 2.2278 - names_loss: 1.5023 - boxes_loss: 0.0093 - names_CategoricalAccuracy: 0.5850 - names_Precision: 0.8248 - names_Recall: 0.3405 - boxes_IoU: 0.6794 - val_loss: 3.4562 - val_names_loss: 2.7188 - val_boxes_loss: 0.0253 - val_names_CategoricalAccuracy: 0.3150 - val_names_Precision: 0.5694 - val_names_Recall: 0.1025 - val_boxes_IoU: 0.5156 - learning_rate: 3.0000e-04 - Trainable: 15.0000 - lapTime: 145.7970
Epoch 20/40
229/229 [==============================] - 146s 639ms/step - loss: 1.9692 - names_loss: 1.2624 - boxes_loss: 0.0082 - names_CategoricalAccuracy: 0.6568 - names_Precision: 0.8743 - names_Recall: 0.4139 - boxes_IoU: 0.6960 - val_loss: 3.2538 - val_names_loss: 2.5586 - val_boxes_loss: 0.0101 - val_names_CategoricalAccuracy: 0.3587 - val_names_Precision: 0.6053 - val_names_Recall: 0.1150 - val_boxes_IoU: 0.6700 - learning_rate: 9.0000e-05 - Trainable: 15.0000 - lapTime: 146.2284
Epoch 21/40
229/229 [==============================] - 146s 639ms/step - loss: 1.8194 - names_loss: 1.1385 - boxes_loss: 0.0078 - names_CategoricalAccuracy: 0.6949 - names_Precision: 0.8933 - names_Recall: 0.4537 - boxes_IoU: 0.7023 - val_loss: 3.1774 - val_names_loss: 2.5053 - val_boxes_loss: 0.0102 - val_names_CategoricalAccuracy: 0.3713 - val_names_Precision: 0.6121 - val_names_Recall: 0.1262 - val_boxes_IoU: 0.6514 - learning_rate: 9.0000e-05 - Trainable: 15.0000 - lapTime: 146.3510
Epoch 22/40
229/229 [==============================] - 145s 635ms/step - loss: 1.7542 - names_loss: 1.0949 - boxes_loss: 0.0075 - names_CategoricalAccuracy: 0.7074 - names_Precision: 0.8962 - names_Recall: 0.4737 - boxes_IoU: 0.7054 - val_loss: 3.1943 - val_names_loss: 2.5451 - val_boxes_loss: 0.0071 - val_names_CategoricalAccuracy: 0.3500 - val_names_Precision: 0.5920 - val_names_Recall: 0.1287 - val_boxes_IoU: 0.7078 - learning_rate: 9.0000e-05 - Trainable: 15.0000 - lapTime: 145.4780
Epoch 23/40
229/229 [==============================] - 146s 638ms/step - loss: 1.6745 - names_loss: 1.0340 - boxes_loss: 0.0075 - names_CategoricalAccuracy: 0.7282 - names_Precision: 0.9141 - names_Recall: 0.5023 - boxes_IoU: 0.7058 - val_loss: 3.1619 - val_names_loss: 2.5293 - val_boxes_loss: 0.0088 - val_names_CategoricalAccuracy: 0.3537 - val_names_Precision: 0.5989 - val_names_Recall: 0.1363 - val_boxes_IoU: 0.6797 - learning_rate: 9.0000e-05 - Trainable: 72.0000 - lapTime: 145.9828
Epoch 24/40
229/229 [==============================] - 146s 636ms/step - loss: 1.6201 - names_loss: 0.9972 - boxes_loss: 0.0075 - names_CategoricalAccuracy: 0.7421 - names_Precision: 0.9138 - names_Recall: 0.5221 - boxes_IoU: 0.7063 - val_loss: 3.1783 - val_names_loss: 2.5643 - val_boxes_loss: 0.0067 - val_names_CategoricalAccuracy: 0.3487 - val_names_Precision: 0.5979 - val_names_Recall: 0.1412 - val_boxes_IoU: 0.7175 - learning_rate: 9.0000e-05 - Trainable: 72.0000 - lapTime: 145.9611
Epoch 25/40
229/229 [==============================] - 146s 639ms/step - loss: 1.5777 - names_loss: 0.9705 - boxes_loss: 0.0072 - names_CategoricalAccuracy: 0.7414 - names_Precision: 0.9115 - names_Recall: 0.5244 - boxes_IoU: 0.7115 - val_loss: 3.1877 - val_names_loss: 2.5867 - val_boxes_loss: 0.0080 - val_names_CategoricalAccuracy: 0.3425 - val_names_Precision: 0.5677 - val_names_Recall: 0.1363 - val_boxes_IoU: 0.6849 - learning_rate: 9.0000e-05 - Trainable: 72.0000 - lapTime: 146.4161
Epoch 26/40
229/229 [==============================] - 146s 636ms/step - loss: 1.4814 - names_loss: 0.8849 - boxes_loss: 0.0068 - names_CategoricalAccuracy: 0.7684 - names_Precision: 0.9249 - names_Recall: 0.5651 - boxes_IoU: 0.7178 - val_loss: 3.1117 - val_names_loss: 2.5184 - val_boxes_loss: 0.0069 - val_names_CategoricalAccuracy: 0.3550 - val_names_Precision: 0.6078 - val_names_Recall: 0.1550 - val_boxes_IoU: 0.7144 - learning_rate: 2.7000e-05 - Trainable: 72.0000 - lapTime: 145.5804
Epoch 27/40
229/229 [==============================] - 1538s 7s/step - loss: 1.4552 - names_loss: 0.8655 - boxes_loss: 0.0064 - names_CategoricalAccuracy: 0.7789 - names_Precision: 0.9277 - names_Recall: 0.5757 - boxes_IoU: 0.7233 - val_loss: 3.1137 - val_names_loss: 2.5258 - val_boxes_loss: 0.0078 - val_names_CategoricalAccuracy: 0.3637 - val_names_Precision: 0.5969 - val_names_Recall: 0.1462 - val_boxes_IoU: 0.6953 - learning_rate: 2.7000e-05 - Trainable: 72.0000 - lapTime: 146.3405
Epoch 28/40
229/229 [==============================] - 145s 632ms/step - loss: 1.4039 - names_loss: 0.8204 - boxes_loss: 0.0064 - names_CategoricalAccuracy: 0.7983 - names_Precision: 0.9339 - names_Recall: 0.5862 - boxes_IoU: 0.7252 - val_loss: 3.1561 - val_names_loss: 2.5749 - val_boxes_loss: 0.0071 - val_names_CategoricalAccuracy: 0.3612 - val_names_Precision: 0.5698 - val_names_Recall: 0.1275 - val_boxes_IoU: 0.7081 - learning_rate: 2.7000e-05 - Trainable: 72.0000 - lapTime: 144.7082
Epoch 29/40
229/229 [==============================] - 1084s 5s/step - loss: 1.3720 - names_loss: 0.7928 - boxes_loss: 0.0062 - names_CategoricalAccuracy: 0.8091 - names_Precision: 0.9450 - names_Recall: 0.6090 - boxes_IoU: 0.7284 - val_loss: 3.0858 - val_names_loss: 2.5071 - val_boxes_loss: 0.0067 - val_names_CategoricalAccuracy: 0.3675 - val_names_Precision: 0.5842 - val_names_Recall: 0.1475 - val_boxes_IoU: 0.7142 - learning_rate: 8.1000e-06 - Trainable: 72.0000 - lapTime: 148.0910
Epoch 30/40
229/229 [==============================] - 147s 642ms/step - loss: 1.3552 - names_loss: 0.7780 - boxes_loss: 0.0062 - names_CategoricalAccuracy: 0.8106 - names_Precision: 0.9388 - names_Recall: 0.6111 - boxes_IoU: 0.7292 - val_loss: 3.0828 - val_names_loss: 2.5060 - val_boxes_loss: 0.0068 - val_names_CategoricalAccuracy: 0.3700 - val_names_Precision: 0.5970 - val_names_Recall: 0.1500 - val_boxes_IoU: 0.7131 - learning_rate: 8.1000e-06 - Trainable: 72.0000 - lapTime: 146.8632
Epoch 31/40
229/229 [==============================] - 148s 645ms/step - loss: 1.3628 - names_loss: 0.7877 - boxes_loss: 0.0061 - names_CategoricalAccuracy: 0.8077 - names_Precision: 0.9364 - names_Recall: 0.6167 - boxes_IoU: 0.7309 - val_loss: 3.0839 - val_names_loss: 2.5093 - val_boxes_loss: 0.0068 - val_names_CategoricalAccuracy: 0.3713 - val_names_Precision: 0.5784 - val_names_Recall: 0.1475 - val_boxes_IoU: 0.7147 - learning_rate: 8.1000e-06 - Trainable: 72.0000 - lapTime: 147.8460
Epoch 32/40
229/229 [==============================] - 146s 638ms/step - loss: 1.3590 - names_loss: 0.7861 - boxes_loss: 0.0060 - names_CategoricalAccuracy: 0.8102 - names_Precision: 0.9365 - names_Recall: 0.6123 - boxes_IoU: 0.7334 - val_loss: 3.0866 - val_names_loss: 2.5141 - val_boxes_loss: 0.0066 - val_names_CategoricalAccuracy: 0.3662 - val_names_Precision: 0.5911 - val_names_Recall: 0.1500 - val_boxes_IoU: 0.7166 - learning_rate: 8.1000e-06 - Trainable: 72.0000 - lapTime: 146.0911
Epoch 33/40
229/229 [==============================] - 146s 637ms/step - loss: 1.3517 - names_loss: 0.7802 - boxes_loss: 0.0060 - names_CategoricalAccuracy: 0.8049 - names_Precision: 0.9410 - names_Recall: 0.6116 - boxes_IoU: 0.7324 - val_loss: 3.0923 - val_names_loss: 2.5203 - val_boxes_loss: 0.0068 - val_names_CategoricalAccuracy: 0.3625 - val_names_Precision: 0.5874 - val_names_Recall: 0.1512 - val_boxes_IoU: 0.7147 - learning_rate: 2.4300e-06 - Trainable: 72.0000 - lapTime: 145.8044
Epoch 34/40
229/229 [==============================] - ETA: 0s - loss: 1.3320 - names_loss: 0.7610 - boxes_loss: 0.0059 - names_CategoricalAccuracy: 0.8158 - names_Precision: 0.9435 - names_Recall: 0.6285 - boxes_IoU: 0.7345

---------------------------------------------------------------
eStop initiated after epoch  33 for poor loss gradient of 0.192
---------------------------------------------------------------

229/229 [==============================] - 146s 639ms/step - loss: 1.3320 - names_loss: 0.7610 - boxes_loss: 0.0059 - names_CategoricalAccuracy: 0.8158 - names_Precision: 0.9435 - names_Recall: 0.6285 - boxes_IoU: 0.7345 - val_loss: 3.0787 - val_names_loss: 2.5069 - val_boxes_loss: 0.0068 - val_names_CategoricalAccuracy: 0.3688 - val_names_Precision: 0.5805 - val_names_Recall: 0.1487 - val_boxes_IoU: 0.7128 - learning_rate: 7.2900e-07 - Trainable: 72.0000 - lapTime: 146.2075

2022-07-30 18:07:20.438553: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.

INFO:tensorflow:Assets written to: ./deployables/ResNet50V2_FINAL/ResNet50V2_FINAL/tfsave/assets

	name	target_size	batch_size	optimizer	patience	slope	factor	uncontrolled	schedule	learning_rate	lossWeights	epochs	verbose	logs	loss	names_loss	boxes_loss	names_CategoricalAccuracy	names_Precision	names_Recall	boxes_IoU	val_loss	val_names_loss	val_boxes_loss	val_names_CategoricalAccuracy	val_names_Precision	val_names_Recall	val_boxes_IoU
0	ResNet50V2_FINAL	(224, 224)	32	Adam	24	0.25	0.3	-15	{22: 0.3}	0.001	{'names': 0.8, 'boxes': 0.19999999999999996}	40	1	{'loss': [13.368462562561035, 8.04307460784912...	1.248332	0.677075	0.006295	0.901575	0.981873	0.726378	0.719278	3.068458	2.496808	0.006688	0.373008	0.688816	0.183267	0.715414

2022-07-30 18:07:31.939469: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

samples from TRAINING SET

samples from TESTING SET

# visualise the final model in full topology
from tensorflow.keras.utils import plot_model
plot_model(pp.model,show_shapes=True,show_layer_activations=True,expand_nested=False)